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This study found that ATR-FTIR spectroscopy combined with machine learning effectively differentiated alopecia areata patients from healthy controls with an AUC of 0.85, and also showed promise in predicting treatment response, particularly through alterations in the Amide I band.

This study successfully used ATR FT-IR spectroscopy to uniquely identify or differentiate human hairs by detecting specific spectral features associated with individual hair characteristics and cosmetic residues on hair surfaces.

This study explored a multidimensional approach to examine body hairs' physico-chemical properties, potentially supporting their use over scalp hairs in forensic and intoxication studies.

Hair removal methods damage the skin barrier and affect substance penetration.

This study indicates potential differences in the hair spectra of individuals with mood disorders, suggesting a promising, yet preliminary, method for noninvasive prediagnosis.